Today, Contextual AI announces the release of advanced datastore configuration options, enabling developers to optimize document processing for RAG-ready outputs tailored to their specific use cases and document types.

Clients can now customize parsing and chunking workflows to maximize RAG performance. Configure heading-depth chunking for granular hierarchy context, use custom prompts for domain-specific image captioning, enable table splitting for complex structured documents, and set precise token limits to optimize retrieval quality.

These configuration options ensure your documents are processed optimally for your RAG system – whether you’re working with technical manuals requiring detailed hierarchical context, visual-heavy documents needing specialized image descriptions, or structured reports with complex tables.

To get started, simply use our updated ingest document API and datastore UI with the new configuration parameters to customize parsing and chunking behavior for your specific documents and use cases.